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PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny
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| Venue: | PLoS Comput Biol 2005 |
| Citations: | 118 - 5 self |
BibTeX
@ARTICLE{Siddharthan_phylogibbs:a,
author = {Rahul Siddharthan and Eric D. Siggia and Erik Van Nimwegen},
title = {PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny},
journal = {PLoS Comput Biol 2005},
year = {},
pages = {67}
}
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Abstract
A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and ‘‘background’ ’ intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markovchain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motiffinding
Keyphrases
motif finder phylogenetic relationship functional site orthologous intergenic region arbitrary number conserved segment central problem related specie algorithm search regulatory protein background intergenic dna arbitrary collection multiple alignment posterior probability spurious conservation gene regulation transcription factor synthetic data evolutionary proximity real data new motif multiple local sequence alignment fuzzy sequence pattern monte carlo markovchain comparative analysis algorithm us multiple sequence alignment promising approach various factor binding site bayesian probabilistic model orthologous sequence site configuration
